In spite of their proven ROI’s for well implemented projects, the proportion of Data Warehouse project failures is fairly high. Failures can take various forms in terms of
The driver behind these failures is that hype & glamour of Data Warehouse has overtaken the diligence (especially , when Data Warehouse and Data Management initiatives and knowledge base still to become a mass awareness and expertise). The diligence required is because Data Warehouse projects are quite different from business systems projects. . While typical business systems projects also may suffer from similar challenges, the intensity of them is much higher in Data Warehouse projects. Here are Data Warehouse challenges, which are unique
| Project Domain |
Transaction/Production Business System |
Data Warehouse System |
| Business Benefits | Tangible benefits in terms of functional capabilities, business processes that will be automated, number of headcounts and reduction etc. Typically, the process getting automated is being done manually, and there is enough visible pain at the ground level and customers. | There is a lesser proportion of initiatives where there is 'heaven will fall', if the project is not done. The benefits can be appreciated by fewer people and much fewer at a ground level. |
| Usage | A business system once implemented, drives the usage as it typically automates a business process. | Data Warehouse platform has a lesser compulsion for usage. Unless there are critical operational reports required. |
| Measure of Usage | One can specify the measure of usage for a business system in terms of processed unit, number of users. | While number of users and the number of queries does represent the level of usage, but it no means suggest that the usage is resulting in delivery of final outcome. |
| Skills and Expertise Requirements- Business | Business system requires the expertise on business process knowledge | More knowledge is required horizontally and vertically. One needs a much higher domain experience as well as cross-functional knowledge for an effective business role fulfillment in Data Warehouse project. The domain expertise also includes all three levels (strategic, managerial and operational). |
| Business Requirements | Ability of defining the business requirements, prioritization is easier as a business system automates an existing process and/OR a severely needed business functionality. | What analysis one needs, why and what one will do post its availability are questions, which demand/challenge the management and strategic thought process. Unlike a business process, analysis for any problem can be done in hundred different ways. Therefore, business requirements tend to change throughout a Data Warehouse project. |
| Business users availability and engagement | Business users are more available and engaged. | Its easier to provide and confirm the requirements of a business process automation, and difficult to define the information and analysis needs. Business users are too busy doing day-to-day work to dwell upon these questions. |
| The demands on the Database | The queries and data access is predictable as they are driven by the mapping of type of transaction, instances etc. A typical transaction touches only certain tables and certain records. Mostly the large and all-encompassing processing happens end of the day processing. | Data-Warehouse cannot predict the kind and incidences of queries on the system. A query can access all the tables and records. |
| Variety of front-end applications | A business system has a pre-defined back-end and front end applications accessing the back-end Database | A data-Warehouse could be having new front-end applications being added on the ongoing basis. This includes OLAP tools, Data mining applications, business performance management applications, online user query and reporting applications. |
| Expectations of flexibility to enhancements | A typical business system has an ever increasing list of enhancements, However, it is expected that the enhancements will take time and system will go through well-spaced out releases. | A Data-Warehouse is expected to provide granular enhancements for most cases. It has to have its design flexible enough to be able to incorporate new dimensions, measures and system sources without unsettling the foundations. |
